@article {58471, title = {Complementarity between mist-netting and low-cost acoustic recorders to sample bats in Amazonian rainforests and savannahs}, journal = {Community Ecology}, year = {2022}, abstract = {

Effective survey methods are paramount to measure changes in species distribution, populations dynamics and to guide conservation. Mist-netting and passive acoustic monitoring are two of the most used techniques to sample bats assemblages. Yet, despite the great potential of low-cost autonomous ultrasound recorders in surveying bat assemblages, we lack thorough assessments of their performance in relation to more established survey methods. Taking advantage of the rich bat fauna of the northeastern Brazilian Amazon, we set out to i) investigate the complementarity of mist-netting and acoustic surveys in sampling bats in forest and savannah habitats in the Savannahs of Amap\á and ii) undertake a cost-effectiveness evaluation of using one, two or three recorders per sampling site to simultaneously survey bat assemblages. The two methods show complementary, and overall species diversity recorded with mist nets was higher than with acoustic recorders. However, species diversity was higher with acoustic recorders than with mist nets when considering a reduced (n\ \<\ 3) number of transects. In addition, we found a gain in species diversity when using more than one acoustic recorder in forest habitats, despite the low cost-effectiveness. However, there were no differences between the diversity using one, two or three acoustic recorders in savannah. Due to possible device malfunction, we recommend the use of at least two acoustic recorders in both habitats to reduce the likelihood of data loss. The use of low-cost bioacoustic recorders in bat surveys can help to address critical knowledge gaps for poorly known aerial-hawking insectivores and support evidence-based conservation strategies.

}, keywords = {acoustic monitoring, Amazonian savannah, Audiomoths, Chiroptera, Methods comparison, Neotropical bats}, doi = {10.1007/s42974-022-00131-5}, author = {William Douglas Carvalho and Jo{\~a}o David Miguel and Bruna da Silva Xavier and Adri{\`a} L{\'o}pez-Baucells~ and Isa{\'\i} Jorge de Castro and Renato Richard Hil{\'a}rio and Jos{\'e} J{\'u}lio de Toledo and Ricardo Rocha and Jorge M. Palmeirim} } @article {57962, title = {Ecological indices in long-term acoustic bat surveys for assessing and monitoring bats{\textquoteright} responses to climatic and land-cover changes}, year = {2020}, abstract = {

Bats are well known for playing an important role in several ecosystem services such as arthropod population control, insect pest suppression in agricultural systems and vector disease control, but also for acting as ecological indicators. Their population dynamics are strongly linked to environmental variations and, in some cases, reflect the health status of ecosystems. Hence, some species have an excellent potential as ecological indicators due to their sensitivity to ecosystem changes. Despite the general decrease of many bat populations worldwide and the recent upsurge in the use of autonomous acoustic detectors, the acoustic monitoring of bat assemblages is still an emerging field in bat research and conservation. Probably due to a general lack of methodological standards and the lack of common ecological indices, few long-term bat acoustic monitoring programs are currently active and data is rarely shared and compared between regions. In this study we propose and adapt a set of different ecological indices that can be used in acoustic surveys designed to detect changes in bat diversity, activity and assemblage composition, all of which can be linked to species\’ climatic and habitat-related preferences. Using a dataset collected during three years of bat monitoring in Catalonia (NE Iberian Peninsula), we used three traditional indices (richness, activity and Shannon diversity) and developed four new ecological indices (Community Thermal Index, Community Precipitation Index, Community Openness Index and Community Specialization Index) that enabled us to study bat communities and compare them at different spatial and temporal scales. Here, we demonstrate the applicability of these indices in bat monitoring programs. We also provide a consistent tool for generating easy-to-interpret ecological indices when monitoring the short- and long-term responses of bats under the current scenario of global change. Using standardized protocols and robust ecological indices enables studies and datasets to be compared, which in turn promotes the development of proper management and conservation measures via international cooperation.

}, keywords = {Bat monitoring, bioacoustics, Chiroptera, Climate change, Ecological indicators, passive acoustic monitoring}, doi = {10.1016/j.ecolind.2019.105849}, url = {https://linkinghub.elsevier.com/retrieve/pii/S1470160X1930843X}, author = {Tuneu-Corral, Carme and Puig-Montserrat, Xavier and Flaquer, Carles and Mas, Maria and Budinski, Ivana and L{\'o}pez-Baucells, Adri{\`a}} } @article {57929, title = {Stronger together: Combining automated classifiers with manual post-validation optimizes the workload vs reliability trade-off of species identification in bat acoustic surveys}, year = {2019}, abstract = {

Owing to major technological advances, bioacoustics has become a burgeoning field in ecological research worldwide. Autonomous passive acoustic recorders are becoming widely used to monitor aerial insectivorous bats, and automatic classifiers have emerged to aid researchers in the daunting task of analysing the resulting massive acoustic datasets. However, the scarcity of comprehensive reference call libraries still hampers their wider application in highly diverse tropical assemblages. Capitalizing on a unique acoustic dataset of \>650,000 bat call sequences collected over a 3-year period in the Brazilian Amazon, the aims of this study were (a) to assess how pre-identified recordings of free-flying and hand-released bats could be used to train an automatic classification algorithm (random forest), and (b) to optimize acoustic analysis protocols by combining automatic classification with visual post-validation, whereby we evaluated the proportion of sound files to be post-validated for different thresholds of classification accuracy. Classifiers were trained at species or sonotype (group of species with similar calls) level. Random forest models confirmed the reliability of using calls of both free-flying and hand-released bats to train custom-built automatic classifiers. To achieve a general classification accuracy of ~85\%, random forest had to be trained with at least 500 pulses per species/sonotype. For seven out of 20 sonotypes, the most abundant in our dataset, we obtained high classification accuracy (\>90\%). Adopting a desired accuracy probability threshold of 95\% for the random forest classifier, we found that the percentage of sound files required for manual post-validation could be reduced by up to 75\%, a significant saving in terms of workload. Combining automatic classification with manual ID through fully customizable classifiers implemented in open-source software as demonstrated here shows great potential to help overcome the acknowledged risks and biases associated with the sole reliance on automatic classification.

}, keywords = {Amazon, bioacoustics, Chiroptera, echolocation, Machine-learning algorithms}, doi = {10.1016/j.ecoinf.2018.11.004}, url = {https://linkinghub.elsevier.com/retrieve/pii/S1574954118300232}, author = {L{\'o}pez-Baucells, Adri{\`a} and Torrent, Laura and Rocha, Ricardo and Paulo E.D. Bobrowiec and Jorge M. Palmeirim and Christoph F.J. Meyer} } @article {57914, title = {Molossid unlimited: extraordinary extension of range and unusual vocalization patterns of the bat, Promops centralisAbstract}, year = {2019}, abstract = {

The big crested mastiff bat, Promops centralis, occurs in Central and South America, but knowledge of its ecology is limited due to its open space hunting strategy, making captures extremely challenging. Notwithstanding, members of the species produce echolocation calls that are easy to identify. After recording calls of P. centralis 1,500 km away from its known range in Brazil, we hypothesized that the distribution range of this species was probably greatly underestimated. To improve the accuracy of P. centralis\’ real distribution, we employed acoustic surveys throughout parts of Brazil, conducted after a bibliographic review to gather additional records, and used MaxEnt to model the species\’ potential distribution. We have found that P. centralis has a much wider distribution in South America than previously thought, adding more than 3.8 million km2 to its former known area. We also describe an unusual vocalization pattern of P. centralis, with individuals emitting at least three very distinct but highly variable calls. This study shows that bioacoustic surveys and species distribution models can complement traditional methodologies in studying species that are difficult to capture, such as P. centralis, potentially contributing to more effective conservation and management plans.

}, keywords = {big crested mastiff bat, Chiroptera, duty cycle, echolocation calls, frequency, MaxEnt, Molossidae, Social calls, species distribution modeling, vocalizations}, doi = {10.1093/jmammal/gyz167}, url = {https://academic.oup.com/jmammal/advance-article/doi/10.1093/jmammal/gyz167/5637356}, author = {Hintze, Frederico and Arias-Aguilar, Adriana and Dias-Silva, Leonardo and Delgado-Jaramillo, Mariana and Silva, Carina Rodrigues and Juc{\'a}, Thays and Mischiatti, Francyne Lyrio and Almeida, M{\'a}rcio and Bezerra, Bruna and Aguiar, Ludmilla M S and Ramos Pereira, Maria Jo{\~a}o and Bernard, Enrico}, editor = {Loeb, Susan} } @article {53351, title = {Stronger together: Combining automated classifiers with manual post-validation optimizes the workload vs reliability trade-off of species identification in bat acoustic surveys}, journal = {Ecological Informatics}, year = {2018}, month = {Jan-11-2018}, abstract = {

Owing to major technological advances, bioacoustics has become a burgeoning field in ecological research worldwide. Autonomous passive acoustic recorders are becoming widely used to monitor aerial insectivorous bats, and automatic classifiers have emerged to aid researchers in the daunting task of analysing the resulting massive acoustic datasets. However, the scarcity of comprehensive reference call libraries still hampers their wider application in highly diverse tropical assemblages. Capitalizing on a unique acoustic dataset of \>650,000 bat call sequences collected over a 3-year period in the Brazilian Amazon, the aims of this study were (a) to assess how pre-identified recordings of free-flying and hand-released bats could be used to train an automatic classification algorithm (random forest), and (b) to optimize acoustic analysis protocols by combining automatic classification with visual post-validation, whereby we evaluated the proportion of sound files to be post-validated for different thresholds of classification accuracy. Classifiers were trained at species or sonotype (group of species with similar calls) level. Random forest models confirmed the reliability of using calls of both free-flying and hand-released bats to train custom-built automatic classifiers. To achieve a general classification accuracy of ~85\%, random forest had to be trained with at least 500 pulses per species/sonotype. For seven out of 20 sonotypes, the most abundant in our dataset, we obtained high classification accuracy (\>90\%). Adopting a desired accuracy probability threshold of 95\% for the random forest classifier, we found that the percentage of sound files required for manual post-validation could be reduced by up to 75\%, a significant saving in terms of workload. Combining automatic classification with manual ID through fully customizable classifiers implemented in open-source software as demonstrated here shows great potential to help overcome the acknowledged risks and biases associated with the sole reliance on automatic classification.

}, keywords = {Amazon, bioacoustics, Chiroptera, echolocation, Machine-learning algorithms}, issn = {15749541}, doi = {10.1016/j.ecoinf.2018.11.004}, url = {https://linkinghub.elsevier.com/retrieve/pii/S1574954118300232https://api.elsevier.com/content/article/PII:S1574954118300232?httpAccept=text/xmlhttps://api.elsevier.com/content/article/PII:S1574954118300232?httpAccept=text/plain}, author = {L{\'o}pez-Baucells, Adri{\`a} and Torrent, Laura and Rocha, Ricardo and Paulo E.D. Bobrowiec and Jorge M. Palmeirim and Christoph F.J. Meyer} }